Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory232.3 B

Variable types

Categorical3
Numeric15

Warnings

objid has constant value "1.23765e+18" Constant
rerun has constant value "301" Constant
ra is highly correlated with fieldHigh correlation
dec is highly correlated with runHigh correlation
u is highly correlated with g and 3 other fieldsHigh correlation
g is highly correlated with u and 3 other fieldsHigh correlation
r is highly correlated with u and 3 other fieldsHigh correlation
i is highly correlated with u and 3 other fieldsHigh correlation
z is highly correlated with u and 3 other fieldsHigh correlation
run is highly correlated with decHigh correlation
camcol is highly correlated with fiberidHigh correlation
field is highly correlated with raHigh correlation
specobjid is highly correlated with plate and 1 other fieldsHigh correlation
plate is highly correlated with specobjid and 1 other fieldsHigh correlation
mjd is highly correlated with specobjid and 1 other fieldsHigh correlation
fiberid is highly correlated with camcolHigh correlation
ra is highly correlated with fieldHigh correlation
dec is highly correlated with runHigh correlation
u is highly correlated with g and 3 other fieldsHigh correlation
g is highly correlated with u and 3 other fieldsHigh correlation
r is highly correlated with u and 3 other fieldsHigh correlation
i is highly correlated with u and 3 other fieldsHigh correlation
z is highly correlated with u and 3 other fieldsHigh correlation
run is highly correlated with dec and 1 other fieldsHigh correlation
camcol is highly correlated with fiberidHigh correlation
field is highly correlated with ra and 1 other fieldsHigh correlation
specobjid is highly correlated with plate and 1 other fieldsHigh correlation
plate is highly correlated with specobjid and 1 other fieldsHigh correlation
mjd is highly correlated with specobjid and 1 other fieldsHigh correlation
fiberid is highly correlated with camcolHigh correlation
ra is highly correlated with fieldHigh correlation
u is highly correlated with gHigh correlation
g is highly correlated with u and 3 other fieldsHigh correlation
r is highly correlated with g and 2 other fieldsHigh correlation
i is highly correlated with g and 2 other fieldsHigh correlation
z is highly correlated with g and 2 other fieldsHigh correlation
field is highly correlated with raHigh correlation
specobjid is highly correlated with plate and 1 other fieldsHigh correlation
plate is highly correlated with specobjid and 1 other fieldsHigh correlation
mjd is highly correlated with specobjid and 1 other fieldsHigh correlation
g is highly correlated with r and 4 other fieldsHigh correlation
r is highly correlated with g and 5 other fieldsHigh correlation
ra is highly correlated with dec and 4 other fieldsHigh correlation
dec is highly correlated with ra and 3 other fieldsHigh correlation
field is highly correlated with ra and 2 other fieldsHigh correlation
u is highly correlated with g and 3 other fieldsHigh correlation
fiberid is highly correlated with plate and 3 other fieldsHigh correlation
z is highly correlated with g and 4 other fieldsHigh correlation
redshift is highly correlated with r and 1 other fieldsHigh correlation
run is highly correlated with ra and 4 other fieldsHigh correlation
class is highly correlated with g and 7 other fieldsHigh correlation
plate is highly correlated with fiberid and 3 other fieldsHigh correlation
camcol is highly correlated with ra and 2 other fieldsHigh correlation
mjd is highly correlated with ra and 6 other fieldsHigh correlation
i is highly correlated with g and 4 other fieldsHigh correlation
specobjid is highly correlated with fiberid and 3 other fieldsHigh correlation
rerun is highly correlated with objid and 1 other fieldsHigh correlation
objid is highly correlated with rerun and 1 other fieldsHigh correlation
class is highly correlated with rerun and 1 other fieldsHigh correlation
ra has unique values Unique
dec has unique values Unique

Reproduction

Analysis started2021-08-04 20:01:46.038433
Analysis finished2021-08-04 20:02:50.654609
Duration1 minute and 4.62 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

objid
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1000.2 KiB
1.23765e+18
10000 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters110000
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.23765e+18
2nd row1.23765e+18
3rd row1.23765e+18
4th row1.23765e+18
5th row1.23765e+18

Common Values

ValueCountFrequency (%)
1.23765e+1810000
100.0%

Length

2021-08-04T22:02:51.067380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-04T22:02:51.172313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.23765e+1810000
100.0%

Most occurring characters

ValueCountFrequency (%)
120000
18.2%
.10000
9.1%
210000
9.1%
310000
9.1%
710000
9.1%
610000
9.1%
510000
9.1%
e10000
9.1%
+10000
9.1%
810000
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80000
72.7%
Other Punctuation10000
 
9.1%
Lowercase Letter10000
 
9.1%
Math Symbol10000
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
120000
25.0%
210000
12.5%
310000
12.5%
710000
12.5%
610000
12.5%
510000
12.5%
810000
12.5%
Other Punctuation
ValueCountFrequency (%)
.10000
100.0%
Lowercase Letter
ValueCountFrequency (%)
e10000
100.0%
Math Symbol
ValueCountFrequency (%)
+10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common100000
90.9%
Latin10000
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
120000
20.0%
.10000
10.0%
210000
10.0%
310000
10.0%
710000
10.0%
610000
10.0%
510000
10.0%
+10000
10.0%
810000
10.0%
Latin
ValueCountFrequency (%)
e10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII110000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120000
18.2%
.10000
9.1%
210000
9.1%
310000
9.1%
710000
9.1%
610000
9.1%
510000
9.1%
e10000
9.1%
+10000
9.1%
810000
9.1%

ra
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.5299866
Minimum8.235100497
Maximum260.8843818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:51.294252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8.235100497
5-th percentile62.11988974
Q1157.3709459
median180.3945139
Q3201.5472789
95-th percentile243.8152597
Maximum260.8843818
Range252.6492813
Interquartile range (IQR)44.17633297

Descriptive statistics

Standard deviation47.78343941
Coefficient of variation (CV)0.2722237969
Kurtosis2.663558747
Mean175.5299866
Median Absolute Deviation (MAD)22.05865535
Skewness-1.227350407
Sum1755299.866
Variance2283.257082
MonotonicityNot monotonic
2021-08-04T22:02:51.514115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242.84419551
 
< 0.1%
187.15293611
 
< 0.1%
183.12847941
 
< 0.1%
122.11761131
 
< 0.1%
222.39275051
 
< 0.1%
161.65082461
 
< 0.1%
187.91634461
 
< 0.1%
152.35063431
 
< 0.1%
228.15372681
 
< 0.1%
203.79887051
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
8.2351004971
< 0.1%
8.2459633511
< 0.1%
8.291367171
< 0.1%
8.3868694141
< 0.1%
8.4878862221
< 0.1%
8.5221192651
< 0.1%
8.7135081731
< 0.1%
8.7145222091
< 0.1%
8.7377766751
< 0.1%
8.7957722651
< 0.1%
ValueCountFrequency (%)
260.88438181
< 0.1%
260.85089751
< 0.1%
260.81118881
< 0.1%
260.76019981
< 0.1%
260.75876321
< 0.1%
260.69911211
< 0.1%
260.68236721
< 0.1%
260.6751911
< 0.1%
260.64456021
< 0.1%
260.60698221
< 0.1%

dec
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.83614798
Minimum-5.382632499
Maximum68.54226541
Zeros0
Zeros (%)0.0%
Negative3774
Negative (%)37.7%
Memory size414.3 KiB
2021-08-04T22:02:51.713001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-5.382632499
5-th percentile-1.773120503
Q1-0.5390350272
median0.40416603
Q335.64939651
95-th percentile66.17550703
Maximum68.54226541
Range73.92489791
Interquartile range (IQR)36.18843153

Descriptive statistics

Standard deviation25.21220658
Coefficient of variation (CV)1.69937686
Kurtosis-0.4061473537
Mean14.83614798
Median Absolute Deviation (MAD)1.079276424
Skewness1.191543668
Sum148361.4798
Variance635.6553606
MonotonicityNot monotonic
2021-08-04T22:02:51.923881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9800320491
 
< 0.1%
1.0688230621
 
< 0.1%
0.6714417251
 
< 0.1%
-1.4337659061
 
< 0.1%
64.00786411
 
< 0.1%
12.813893931
 
< 0.1%
-3.4162793491
 
< 0.1%
51.967349791
 
< 0.1%
51.256576641
 
< 0.1%
0.3435921161
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
-5.3826324991
< 0.1%
-5.3787936941
< 0.1%
-5.3719884961
< 0.1%
-5.354571981
< 0.1%
-5.34962081
< 0.1%
-5.3487155881
< 0.1%
-5.3473444631
< 0.1%
-5.3398349451
< 0.1%
-5.3192841831
< 0.1%
-5.3120738021
< 0.1%
ValueCountFrequency (%)
68.542265411
< 0.1%
68.540566931
< 0.1%
68.532006811
< 0.1%
68.483235951
< 0.1%
68.480161071
< 0.1%
68.464126131
< 0.1%
68.461302541
< 0.1%
68.460756341
< 0.1%
68.455732681
< 0.1%
68.440465061
< 0.1%

u
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9730
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.61935536
Minimum12.98897
Maximum19.5999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:52.183396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12.98897
5-th percentile16.971334
Q118.178035
median18.853095
Q319.2592325
95-th percentile19.534464
Maximum19.5999
Range6.61093
Interquartile range (IQR)1.0811975

Descriptive statistics

Standard deviation0.8286560094
Coefficient of variation (CV)0.04450508589
Kurtosis1.432495436
Mean18.61935536
Median Absolute Deviation (MAD)0.48623
Skewness-1.219794813
Sum186193.5536
Variance0.6866707819
MonotonicityNot monotonic
2021-08-04T22:02:52.375287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.25753
 
< 0.1%
18.996973
 
< 0.1%
19.499943
 
< 0.1%
18.902123
 
< 0.1%
19.535073
 
< 0.1%
18.9843
 
< 0.1%
19.56353
 
< 0.1%
19.490372
 
< 0.1%
19.558542
 
< 0.1%
18.954232
 
< 0.1%
Other values (9720)9973
99.7%
ValueCountFrequency (%)
12.988971
< 0.1%
13.551781
< 0.1%
13.993711
< 0.1%
14.458561
< 0.1%
14.728251
< 0.1%
14.728581
< 0.1%
14.846261
< 0.1%
14.873451
< 0.1%
15.0021
< 0.1%
15.079921
< 0.1%
ValueCountFrequency (%)
19.59991
< 0.1%
19.599751
< 0.1%
19.599711
< 0.1%
19.59961
< 0.1%
19.599341
< 0.1%
19.599261
< 0.1%
19.599231
< 0.1%
19.599121
< 0.1%
19.599081
< 0.1%
19.599062
< 0.1%

g
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9817
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.37193149
Minimum12.79955
Maximum19.91897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:52.593133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12.79955
5-th percentile15.6195515
Q116.8151
median17.495135
Q318.010145
95-th percentile18.821572
Maximum19.91897
Range7.11942
Interquartile range (IQR)1.195045

Descriptive statistics

Standard deviation0.9454571753
Coefficient of variation (CV)0.05442441308
Kurtosis0.4439763386
Mean17.37193149
Median Absolute Deviation (MAD)0.582765
Skewness-0.5362927437
Sum173719.3149
Variance0.8938892703
MonotonicityNot monotonic
2021-08-04T22:02:52.798629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.754783
 
< 0.1%
17.556233
 
< 0.1%
17.607663
 
< 0.1%
18.31913
 
< 0.1%
17.536122
 
< 0.1%
17.755372
 
< 0.1%
18.695662
 
< 0.1%
18.115522
 
< 0.1%
18.448622
 
< 0.1%
18.139362
 
< 0.1%
Other values (9807)9976
99.8%
ValueCountFrequency (%)
12.799551
< 0.1%
13.080551
< 0.1%
13.205551
< 0.1%
13.250141
< 0.1%
13.437281
< 0.1%
13.469481
< 0.1%
13.631571
< 0.1%
13.678991
< 0.1%
13.687951
< 0.1%
13.710161
< 0.1%
ValueCountFrequency (%)
19.918971
< 0.1%
19.738691
< 0.1%
19.682321
< 0.1%
19.672241
< 0.1%
19.659931
< 0.1%
19.641281
< 0.1%
19.605521
< 0.1%
19.591611
< 0.1%
19.586511
< 0.1%
19.579731
< 0.1%

r
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9852
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.84096332
Minimum12.4316
Maximum24.80204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:53.471886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12.4316
5-th percentile15.0228965
Q116.1733325
median16.85877
Q317.512675
95-th percentile18.7027535
Maximum24.80204
Range12.37044
Interquartile range (IQR)1.3393425

Descriptive statistics

Standard deviation1.067764349
Coefficient of variation (CV)0.06340280715
Kurtosis0.7524338686
Mean16.84096332
Median Absolute Deviation (MAD)0.66711
Skewness-0.02167290244
Sum168409.6332
Variance1.140120706
MonotonicityNot monotonic
2021-08-04T22:02:53.738730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.489783
 
< 0.1%
15.999863
 
< 0.1%
15.488182
 
< 0.1%
17.216362
 
< 0.1%
17.551512
 
< 0.1%
17.351222
 
< 0.1%
18.046442
 
< 0.1%
16.586352
 
< 0.1%
15.813392
 
< 0.1%
16.68982
 
< 0.1%
Other values (9842)9978
99.8%
ValueCountFrequency (%)
12.43161
< 0.1%
12.444271
< 0.1%
12.482381
< 0.1%
12.492451
< 0.1%
12.640291
< 0.1%
12.643791
< 0.1%
12.685821
< 0.1%
12.722051
< 0.1%
12.904781
< 0.1%
13.040761
< 0.1%
ValueCountFrequency (%)
24.802041
< 0.1%
24.802031
< 0.1%
20.407271
< 0.1%
20.065021
< 0.1%
19.998861
< 0.1%
19.988341
< 0.1%
19.947251
< 0.1%
19.880021
< 0.1%
19.874081
< 0.1%
19.852481
< 0.1%

i
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9890
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.58357925
Minimum11.94721
Maximum28.17963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:53.942638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11.94721
5-th percentile14.7385905
Q115.853705
median16.554985
Q317.25855
95-th percentile18.6197215
Maximum28.17963
Range16.23242
Interquartile range (IQR)1.404845

Descriptive statistics

Standard deviation1.141804819
Coefficient of variation (CV)0.06885153091
Kurtosis1.831684157
Mean16.58357925
Median Absolute Deviation (MAD)0.701745
Skewness0.2864419057
Sum165835.7925
Variance1.303718245
MonotonicityNot monotonic
2021-08-04T22:02:54.164507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.730852
 
< 0.1%
17.109382
 
< 0.1%
16.028612
 
< 0.1%
17.167762
 
< 0.1%
16.983372
 
< 0.1%
16.89952
 
< 0.1%
15.821512
 
< 0.1%
18.170742
 
< 0.1%
15.90582
 
< 0.1%
14.944532
 
< 0.1%
Other values (9880)9980
99.8%
ValueCountFrequency (%)
11.947211
< 0.1%
12.065441
< 0.1%
12.184351
< 0.1%
12.200131
< 0.1%
12.205661
< 0.1%
12.239911
< 0.1%
12.242081
< 0.1%
12.433691
< 0.1%
12.580041
< 0.1%
12.694331
< 0.1%
ValueCountFrequency (%)
28.179631
< 0.1%
24.361811
< 0.1%
24.356821
< 0.1%
24.225261
< 0.1%
22.896141
< 0.1%
20.793911
< 0.1%
20.450511
< 0.1%
20.263341
< 0.1%
20.239621
< 0.1%
20.237811
< 0.1%

z
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9896
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.42283316
Minimum11.61041
Maximum22.83306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:54.383997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11.61041
5-th percentile14.518281
Q115.618285
median16.389945
Q317.1414475
95-th percentile18.5771985
Maximum22.83306
Range11.22265
Interquartile range (IQR)1.5231625

Descriptive statistics

Standard deviation1.203188048
Coefficient of variation (CV)0.07326312311
Kurtosis0.3699576617
Mean16.42283316
Median Absolute Deviation (MAD)0.761145
Skewness0.2143128273
Sum164228.3316
Variance1.447661478
MonotonicityNot monotonic
2021-08-04T22:02:54.588878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.51122
 
< 0.1%
16.429152
 
< 0.1%
16.220532
 
< 0.1%
16.77532
 
< 0.1%
16.436352
 
< 0.1%
16.297742
 
< 0.1%
15.726562
 
< 0.1%
15.108642
 
< 0.1%
16.608952
 
< 0.1%
17.375442
 
< 0.1%
Other values (9886)9980
99.8%
ValueCountFrequency (%)
11.610411
< 0.1%
11.757421
< 0.1%
11.791231
< 0.1%
11.806451
< 0.1%
11.969461
< 0.1%
11.980451
< 0.1%
11.980741
< 0.1%
12.10421
< 0.1%
12.262621
< 0.1%
12.340991
< 0.1%
ValueCountFrequency (%)
22.833061
< 0.1%
22.826911
< 0.1%
21.340411
< 0.1%
20.79611
< 0.1%
20.64741
< 0.1%
20.622221
< 0.1%
20.59681
< 0.1%
20.566061
< 0.1%
20.561911
< 0.1%
20.457241
< 0.1%

run
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean981.0348
Minimum308
Maximum1412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:54.777777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum308
5-th percentile752
Q1752
median756
Q31331
95-th percentile1402
Maximum1412
Range1104
Interquartile range (IQR)579

Descriptive statistics

Standard deviation273.305024
Coefficient of variation (CV)0.2785885108
Kurtosis-1.558851438
Mean981.0348
Median Absolute Deviation (MAD)4
Skewness0.4125548577
Sum9810348
Variance74695.63615
MonotonicityNot monotonic
2021-08-04T22:02:54.925683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
7563060
30.6%
7522086
20.9%
1345915
 
9.2%
1350540
 
5.4%
1140527
 
5.3%
745453
 
4.5%
1035396
 
4.0%
1412347
 
3.5%
1302246
 
2.5%
1231245
 
2.5%
Other values (13)1185
 
11.8%
ValueCountFrequency (%)
30831
 
0.3%
7274
 
< 0.1%
745453
 
4.5%
7522086
20.9%
7563060
30.6%
1035396
 
4.0%
1045112
 
1.1%
11191
 
< 0.1%
1140527
 
5.3%
1231245
 
2.5%
ValueCountFrequency (%)
1412347
 
3.5%
141110
 
0.1%
1404137
 
1.4%
140249
 
0.5%
13564
 
< 0.1%
1350540
5.4%
1345915
9.2%
1336182
 
1.8%
1334212
 
2.1%
13321
 
< 0.1%

rerun
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size922.1 KiB
301
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row301
2nd row301
3rd row301
4th row301
5th row301

Common Values

ValueCountFrequency (%)
30110000
100.0%

Length

2021-08-04T22:02:55.326865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-04T22:02:55.440787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
30110000
100.0%

Most occurring characters

ValueCountFrequency (%)
310000
33.3%
010000
33.3%
110000
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
310000
33.3%
010000
33.3%
110000
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
310000
33.3%
010000
33.3%
110000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
310000
33.3%
010000
33.3%
110000
33.3%

camcol
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6487
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:55.523739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.666183041
Coefficient of variation (CV)0.4566511473
Kurtosis-1.222048975
Mean3.6487
Median Absolute Deviation (MAD)1
Skewness-0.1002196014
Sum36487
Variance2.776165927
MonotonicityNot monotonic
2021-08-04T22:02:55.696640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
41834
18.3%
51827
18.3%
61769
17.7%
21712
17.1%
31560
15.6%
11298
13.0%
ValueCountFrequency (%)
11298
13.0%
21712
17.1%
31560
15.6%
41834
18.3%
51827
18.3%
61769
17.7%
ValueCountFrequency (%)
61769
17.7%
51827
18.3%
41834
18.3%
31560
15.6%
21712
17.1%
11298
13.0%

field
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct703
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.3801
Minimum11
Maximum768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:55.890527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile32.95
Q1184
median299
Q3414
95-th percentile582
Maximum768
Range757
Interquartile range (IQR)230

Descriptive statistics

Standard deviation162.5777629
Coefficient of variation (CV)0.5376602589
Kurtosis-0.4780469784
Mean302.3801
Median Absolute Deviation (MAD)115
Skewness0.2497952991
Sum3023801
Variance26431.52898
MonotonicityNot monotonic
2021-08-04T22:02:56.110788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30162
 
0.6%
30258
 
0.6%
30456
 
0.6%
30555
 
0.5%
30754
 
0.5%
30954
 
0.5%
31251
 
0.5%
31151
 
0.5%
30050
 
0.5%
31048
 
0.5%
Other values (693)9461
94.6%
ValueCountFrequency (%)
1124
0.2%
1221
0.2%
1327
0.3%
1425
0.2%
1525
0.2%
1621
0.2%
1730
0.3%
1824
0.2%
1933
0.3%
2023
0.2%
ValueCountFrequency (%)
7683
< 0.1%
7675
0.1%
7663
< 0.1%
7654
< 0.1%
7645
0.1%
7634
< 0.1%
7621
 
< 0.1%
7111
 
< 0.1%
7102
 
< 0.1%
7091
 
< 0.1%

specobjid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6349
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.64502157 × 1018
Minimum2.99578 × 1017
Maximum9.46883 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:56.342664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.99578 × 1017
5-th percentile3.07484 × 1017
Q13.3892475 × 1017
median4.96658 × 1017
Q32.8813 × 1018
95-th percentile6.44927 × 1018
Maximum9.46883 × 1018
Range9.169252 × 1018
Interquartile range (IQR)2.54237525 × 1018

Descriptive statistics

Standard deviation2.013998493 × 1018
Coefficient of variation (CV)1.22429914
Kurtosis2.965372237
Mean1.64502157 × 1018
Median Absolute Deviation (MAD)1.83481 × 1017
Skewness1.794627238
Sum1.64502157 × 1022
Variance4.056189929 × 1036
MonotonicityNot monotonic
2021-08-04T22:02:56.547535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.72223 × 101818
 
0.2%
2.88122 × 101818
 
0.2%
2.88127 × 101818
 
0.2%
2.88012 × 101817
 
0.2%
3.22241 × 101817
 
0.2%
2.88011 × 101816
 
0.2%
2.88125 × 101816
 
0.2%
3.22237 × 101816
 
0.2%
3.25958 × 101816
 
0.2%
2.88008 × 101816
 
0.2%
Other values (6339)9832
98.3%
ValueCountFrequency (%)
2.99578 × 10171
< 0.1%
2.99582 × 10171
< 0.1%
2.99583 × 10171
< 0.1%
2.99585 × 10171
< 0.1%
2.99588 × 10171
< 0.1%
2.99595 × 10171
< 0.1%
2.99597 × 10171
< 0.1%
2.99598 × 10171
< 0.1%
2.996 × 10171
< 0.1%
2.99603 × 10172
< 0.1%
ValueCountFrequency (%)
9.46883 × 10181
< 0.1%
9.33501 × 10181
< 0.1%
9.32043 × 10181
< 0.1%
9.32039 × 10181
< 0.1%
9.31932 × 10182
< 0.1%
9.31923 × 10181
< 0.1%
9.31799 × 10181
< 0.1%
9.31797 × 10181
< 0.1%
9.31796 × 10181
< 0.1%
9.31795 × 10181
< 0.1%

class
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size940.8 KiB
GALAXY
4998 
STAR
4152 
QSO
850 

Length

Max length6
Median length4
Mean length4.9146
Min length3

Characters and Unicode

Total characters49146
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTAR
2nd rowSTAR
3rd rowGALAXY
4th rowSTAR
5th rowSTAR

Common Values

ValueCountFrequency (%)
GALAXY4998
50.0%
STAR4152
41.5%
QSO850
 
8.5%

Length

2021-08-04T22:02:56.970083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-04T22:02:57.083020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
galaxy4998
50.0%
star4152
41.5%
qso850
 
8.5%

Most occurring characters

ValueCountFrequency (%)
A14148
28.8%
S5002
 
10.2%
G4998
 
10.2%
L4998
 
10.2%
X4998
 
10.2%
Y4998
 
10.2%
T4152
 
8.4%
R4152
 
8.4%
Q850
 
1.7%
O850
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49146
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A14148
28.8%
S5002
 
10.2%
G4998
 
10.2%
L4998
 
10.2%
X4998
 
10.2%
Y4998
 
10.2%
T4152
 
8.4%
R4152
 
8.4%
Q850
 
1.7%
O850
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin49146
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A14148
28.8%
S5002
 
10.2%
G4998
 
10.2%
L4998
 
10.2%
X4998
 
10.2%
Y4998
 
10.2%
T4152
 
8.4%
R4152
 
8.4%
Q850
 
1.7%
O850
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII49146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A14148
28.8%
S5002
 
10.2%
G4998
 
10.2%
L4998
 
10.2%
X4998
 
10.2%
Y4998
 
10.2%
T4152
 
8.4%
R4152
 
8.4%
Q850
 
1.7%
O850
 
1.7%

redshift
Real number (ℝ)

HIGH CORRELATION

Distinct9637
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1437257118
Minimum-0.004136078
Maximum5.353854
Zeros19
Zeros (%)0.2%
Negative1919
Negative (%)19.2%
Memory size414.3 KiB
2021-08-04T22:02:57.225954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.004136078
5-th percentile-0.00031906605
Q18.09 × 10-5
median0.042590705
Q30.09257851
95-th percentile1.0517683
Maximum5.353854
Range5.357990078
Interquartile range (IQR)0.09249761

Descriptive statistics

Standard deviation0.3887740358
Coefficient of variation (CV)2.704972067
Kurtosis20.55006723
Mean0.1437257118
Median Absolute Deviation (MAD)0.042554005
Skewness4.265729143
Sum1437.257118
Variance0.1511452509
MonotonicityNot monotonic
2021-08-04T22:02:57.453806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019
 
0.2%
-1.98 × 10-56
 
0.1%
-6.16 × 10-55
 
0.1%
-7.32 × 10-54
 
< 0.1%
-8.02 × 10-54
 
< 0.1%
-5.15 × 10-54
 
< 0.1%
-4.82 × 10-54
 
< 0.1%
4.36 × 10-54
 
< 0.1%
6.18 × 10-54
 
< 0.1%
-8.73 × 10-54
 
< 0.1%
Other values (9627)9942
99.4%
ValueCountFrequency (%)
-0.0041360782
< 0.1%
-0.0033276491
< 0.1%
-0.0029651761
< 0.1%
-0.0020545981
< 0.1%
-0.0014432181
< 0.1%
-0.0014205851
< 0.1%
-0.0014192391
< 0.1%
-0.0013953431
< 0.1%
-0.001348861
< 0.1%
-0.0013460091
< 0.1%
ValueCountFrequency (%)
5.3538541
< 0.1%
4.1061831
< 0.1%
3.8965861
< 0.1%
3.8294761
< 0.1%
3.0142941
< 0.1%
2.9344671
< 0.1%
2.8825471
< 0.1%
2.8323951
< 0.1%
2.7911881
< 0.1%
2.7445961
< 0.1%

plate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct487
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1460.9864
Minimum266
Maximum8410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:57.675678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum266
5-th percentile273
Q1301
median441
Q32559
95-th percentile5728
Maximum8410
Range8144
Interquartile range (IQR)2258

Descriptive statistics

Standard deviation1788.778371
Coefficient of variation (CV)1.224363465
Kurtosis2.965295673
Mean1460.9864
Median Absolute Deviation (MAD)163
Skewness1.794609444
Sum14609864
Variance3199728.06
MonotonicityNot monotonic
2021-08-04T22:02:57.885557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2558222
 
2.2%
2559221
 
2.2%
2895166
 
1.7%
276156
 
1.6%
7456139
 
1.4%
278131
 
1.3%
2389129
 
1.3%
288128
 
1.3%
275127
 
1.3%
2445123
 
1.2%
Other values (477)8458
84.6%
ValueCountFrequency (%)
26645
 
0.4%
26761
0.6%
26873
0.7%
26969
0.7%
27053
0.5%
27182
0.8%
27276
0.8%
27386
0.9%
274101
1.0%
275127
1.3%
ValueCountFrequency (%)
84101
 
< 0.1%
82911
 
< 0.1%
82782
< 0.1%
82773
< 0.1%
82764
< 0.1%
81982
< 0.1%
81964
< 0.1%
81954
< 0.1%
81941
 
< 0.1%
75621
 
< 0.1%

mjd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct355
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52943.5333
Minimum51578
Maximum57481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:58.124419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum51578
5-th percentile51637
Q151900
median51997
Q354468
95-th percentile56221.15
Maximum57481
Range5903
Interquartile range (IQR)2568

Descriptive statistics

Standard deviation1511.150651
Coefficient of variation (CV)0.02854268607
Kurtosis-0.2206599332
Mean52943.5333
Median Absolute Deviation (MAD)316
Skewness1.039610203
Sum529435333
Variance2283576.289
MonotonicityNot monotonic
2021-08-04T22:02:58.361284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52000312
 
3.1%
51909222
 
2.2%
54140222
 
2.2%
54208221
 
2.2%
51908214
 
2.1%
51910204
 
2.0%
51900197
 
2.0%
51984179
 
1.8%
54567166
 
1.7%
51693160
 
1.6%
Other values (345)7903
79.0%
ValueCountFrequency (%)
515781
 
< 0.1%
5160861
0.6%
5160942
0.4%
5161293
0.9%
5161318
 
0.2%
5161468
0.7%
5161579
0.8%
5163045
0.4%
5163373
0.7%
5163728
 
0.3%
ValueCountFrequency (%)
574811
 
< 0.1%
574011
 
< 0.1%
573915
0.1%
573742
 
< 0.1%
573464
< 0.1%
570932
 
< 0.1%
570733
< 0.1%
570713
< 0.1%
570674
< 0.1%
570611
 
< 0.1%

fiberid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct892
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.0694
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size414.3 KiB
2021-08-04T22:02:58.585154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q1186.75
median351
Q3510
95-th percentile636
Maximum1000
Range999
Interquartile range (IQR)323.25

Descriptive statistics

Standard deviation206.2981485
Coefficient of variation (CV)0.5842991449
Kurtosis-0.3085428407
Mean353.0694
Median Absolute Deviation (MAD)162
Skewness0.3080532504
Sum3530694
Variance42558.92608
MonotonicityNot monotonic
2021-08-04T22:02:58.806028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15529
 
0.3%
13828
 
0.3%
45427
 
0.3%
1126
 
0.3%
24926
 
0.3%
56825
 
0.2%
50625
 
0.2%
26825
 
0.2%
52725
 
0.2%
30025
 
0.2%
Other values (882)9739
97.4%
ValueCountFrequency (%)
112
0.1%
211
0.1%
313
0.1%
415
0.1%
519
0.2%
623
0.2%
711
0.1%
813
0.1%
913
0.1%
1022
0.2%
ValueCountFrequency (%)
10001
< 0.1%
9991
< 0.1%
9981
< 0.1%
9972
< 0.1%
9962
< 0.1%
9951
< 0.1%
9942
< 0.1%
9931
< 0.1%
9921
< 0.1%
9891
< 0.1%

Interactions

2021-08-04T22:01:53.652139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:54.019219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:54.336267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:54.650086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:54.968908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:55.236527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:55.561339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:55.819192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:56.099030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:56.349887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:56.603743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:56.755677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:56.912254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:57.175103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:57.434954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:57.668820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:57.937668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:58.451905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:58.708058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:58.992897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:59.274391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:59.573219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:01:59.840065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:00.080927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:00.327785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:00.608309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:00.821188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:01.051054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:01.295914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:01.549770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:01.775391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:01.985271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:02.228130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:02.469812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:02.710673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:02.927558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:03.167412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:03.399277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:03.672119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:03.898991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:04.141852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:04.405701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:04.614248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:04.871108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:05.103967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:05.362817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:05.669284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:05.968112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:06.229961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:06.525790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:06.786474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:07.059315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:07.292181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:07.649975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:07.920819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:08.202657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:08.475500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:08.834296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:09.345998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:09.581864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:09.839715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:10.054591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:10.303448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:10.502335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:10.728204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:10.923093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:11.163953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:11.397821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:11.616693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:11.806585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:12.052443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:12.227343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:12.416233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:12.638105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:12.852982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:13.069858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:13.279736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:13.519598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:13.731477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:13.952350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:14.193211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:14.434074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:14.671936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:14.886822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:15.188641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:15.516451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:15.786295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:15.985182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:16.274014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:16.534866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:16.783722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:16.966617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:17.176495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:17.416358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:17.663215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:17.932061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:18.206903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:18.437771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:18.713618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:18.953476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:19.237311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:19.492164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:19.707040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:19.972887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:20.265719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:20.490589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:20.764432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:21.034276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:21.307120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:21.909774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:22.160631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:22.423479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:22.757285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:23.039123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:23.295996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:23.577832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:23.850675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:24.113524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:24.463299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:24.782096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:25.122228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:25.483343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:25.921091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:26.309867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:26.633681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:26.970489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:27.302297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:27.609542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:27.890379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:28.120259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:28.442166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:28.711023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:28.943912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:29.250790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:29.492834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:29.733711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:30.067772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:30.302636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:30.520510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:30.754380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:30.999240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:31.309121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:31.570971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:31.790846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:32.059690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:32.306563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:32.537996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:32.759869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:33.044706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:33.278371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:33.498257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:33.688134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:33.897015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:34.122883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:34.349754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:34.541645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:34.740530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:34.929420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:35.129319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:35.327192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:35.534074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:35.751946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:35.941836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:36.167265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:36.350163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:36.539856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:36.735742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:36.958618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:37.160499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:37.350396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:37.556079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:37.766954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:38.293653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:38.519736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:38.702299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:38.901181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:39.091073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:39.276965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:39.481849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:39.695726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:39.884632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:40.103505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:40.334360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:40.594209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:40.844079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:41.068744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:41.380563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:41.586460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:41.791341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:42.054919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:42.289790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:42.494667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:42.692132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:42.950982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:43.166843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:43.380720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:43.583604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:43.782491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:43.984374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:44.203249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:44.430117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:44.645993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:44.827904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:45.025775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:45.203337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:45.431205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:45.649082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:45.833689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:46.100526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:46.296416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:46.491306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:46.683185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:46.909042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:47.123919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:47.334796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:47.568661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:47.821515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:48.017404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:48.196313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:48.389188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:48.595669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:48.779563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:48.960473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:49.207318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-04T22:02:49.417734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-08-04T22:02:59.012923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-04T22:02:59.409679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-04T22:02:59.871126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-04T22:03:00.260893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-04T22:03:00.557374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-04T22:02:49.778527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-04T22:02:50.388762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

objidradecugrizrunreruncamcolfieldspecobjidclassredshiftplatemjdfiberid
01.237650e+18183.5313260.08969319.4740617.0424015.9469915.5034215.2253175230142673.722360e+18STAR-0.000009330654922491
11.237650e+18183.5983700.13528518.6628017.2144916.6763716.4892216.3915075230142673.638140e+17STAR-0.00005532351615541
21.237650e+18183.6802070.12618519.3829818.1916917.4742817.0873216.8012575230142683.232740e+17GALAXY0.12311128752023513
31.237650e+18183.8705290.04991117.7653616.6027216.1611615.9823315.9043875230142693.722370e+18STAR-0.000111330654922510
41.237650e+18183.8832880.10255717.5502516.2634216.4386916.5549216.6132675230142693.722370e+18STAR0.000590330654922512
51.237650e+18183.8471740.17369419.4313318.4677918.1645118.0147518.0415575230142693.649550e+17STAR0.00031532451666594
61.237650e+18183.8643790.01920119.3832217.8899517.1053716.6639316.3695575230142693.232870e+17GALAXY0.10024228752023559
71.237650e+18183.9000810.18747318.9799317.8449617.3802217.2067317.0707175230142693.722370e+18STAR0.000315330654922515
81.237650e+18183.9245880.09724617.9061616.9717216.6754116.5377616.4759675230142703.638290e+17STAR0.00008932351615595
91.237650e+18183.9734980.08162618.6724917.7137517.4936217.2828417.2264475230142703.243690e+17GALAXY0.04050828852000400

Last rows

objidradecugrizrunreruncamcolfieldspecobjidclassredshiftplatemjdfiberid
99901.237650e+18131.11557051.44713618.1788016.7198916.0400415.6633815.38130134530131605.033570e+17GALAXY0.04490944751877290
99911.237650e+18131.11328451.49071018.5880616.6141815.6240715.1911314.83131134530131605.011930e+17GALAXY0.09698644551873609
99921.237650e+18131.20282051.49901119.0261418.1393617.8269717.7131817.71018134530131618.211260e+18STAR0.000315729356741254
99931.237650e+18131.39891551.53370617.7961316.0262115.1595514.7148814.33840134530131615.033460e+17GALAXY0.05551544751877249
99941.237650e+18131.17579151.67967519.5200018.4619518.1131718.0224517.99046134530131618.211260e+18STAR-0.000056729356741259
99951.237650e+18131.31641351.53954718.8177717.4705316.9150816.6830516.50570134530131615.033450e+17GALAXY0.02758344751877246
99961.237650e+18131.30608351.67134118.2725517.4384917.0769216.7166116.69897134530131625.033400e+17GALAXY0.11777244751877228
99971.237650e+18131.55256251.66698618.7581817.7778417.5187217.4330217.42048134530131628.222620e+18STAR-0.000402730357013622
99981.237650e+18131.47715151.75306818.8828717.9106817.5315217.3628417.13988134530131635.033400e+17GALAXY0.01401944751877229
99991.237650e+18131.66501251.80530719.2758617.3782916.3054215.8354815.50588134530131635.033410e+17GALAXY0.11841744751877233